Ai techniques for game programming pdf download

RTS games have units with different characteristics in weaponry, movement and … Expand. View 1 excerpt, cites methods. Artificial Intelligence and Games. View 1 excerpt, cites background. Neuroevolution for RTS Micro. We investigate heuristic search algorithms to generate high quality micro management in combat scenarios for real-time strategy games. Macro and micro management are two key aspects of real-time … Expand. View 1 excerpt. Related Papers.

As this book goes to press, developers can look around and find the game industry exploding with activity, reaching out to new audiences, and evolving like never before. As new consoles and PC platforms flood the market, developers find themselves faced with an abundance of riches in terms of memory, CPU speeds, connectivity options, and video resolutions. These new capabilities provide the game developer with endless possibilities—and endless decisions for trade-offs and focus.

Should the new game step up video resolution another notch, or should we focus on making the collisions more realistic? Great game AI is one obvious way to make your game stand out from the crowd, and the flood of books and articles on the subject bears this out. Developers are doing everything they can to investigate new AI technologies that they can assimilate to help build better, smarter game AIs. Of course, these new AIs have to help make the game sell better, too.

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This site uses Akismet to reduce spam. No longer did gamers have to muck about endlessly with their config. Now we could just install, click, and play. It was a revelation. Windows 98 Onward Successive generations of Windows have built upon the success of Windows Windows has become more stable, more user friendly, and easier to program for.

DOS is a thing of the distant past, and nowadays, all games are written to run under the Windows environment. Hello World! Please see the source code on the CD if further clarification is required. The range is defined by start and end iterators.

As with erase , you can choose to insert a single element at a position pointed to by an iterator or you can insert a range of elements. Here is a simple example, which inserts the first four elements in vecInt1 at position five in vecInt2. Select two random points and reverse the cities between them.

Alternative Permutation Crossover Operators As with mutation operators, inventing crossover operators that spawn valid permutations has been a popular sport amongst genetic algorithm enthusiasts. Here are the descriptions and code for a couple of the better ones. Team LRN 5. Building a Better Genetic Algorithm Order-Based Crossover OBX To perform order-based crossover, several cities are chosen at random from one parent and then the order of those cities is imposed on the respective cities in the other parent.

Now, impose the order—5, 0, then 1—on the same cities in Parent2 to give Offspring1 like so: Offspring1: 3. Now the same sequence of actions is performed on the other parent.

Using the same positions as the first, Parent1: 2. The reason that this constant is present is to make the game more fun. Let me show you what I mean… As I mentioned earlier in the section on physics, when programming a game, units of distance are measured in pixels and not in meters. The lunar lander starts its descent approximately pixels above the landing pad, so this represents meters in the real world.

From the equation u the start velocity is zero, so you can simplify to and then shuffle using a bit of algebra. Putting in the numbers gives you Team LRN 6.

Moon Landings Made Easy a time of over 19 seconds to reach the pad. In this case, 19 seconds is just too long. It would be boring take the scaling off and try it to see just how tedious it is! So, to compensate, a scaling factor is introduced. In effect, this is equivalent to the lander starting its descent from a lower altitude. The physics remain exactly the same, but now the lander is much more fun to control.

Now, the following tests if the lander has crashed or made a successful landing. If a vertex is found to be below the ground, the program checks to see if the module has landed gracefully or crashed like an albatross. The UpdateShip function then plays an appropriate wav file and returns. Moon Landings Made Easy All the tolerances for a successful landing can be found in defines.

First, the encoding… Encoding the Genome You have already seen how candidate solutions may be encoded as binary bit strings or as permutations of integers, and you may have already guessed that you can just as easily encode some problems as a series of real numbers.

For now though, the important thing to note is that you must ensure that crossover and mutation operators can be applied in a way that is meaningful to the problem.

So then, how do you encode the lander problem? You can apply a rotational force to the right. Team LRN A Genetic Algorithm Controlled Lander Each of these four controls is applied for a certain period of time, which is measured in the fraction of a second it takes to update each frame.

Therefore, an encoding has to be found that incorporates both an action and a duration. Figure 6. As you can see, each gene contains a data pair. The first half of the gene indicates the action the ship should take, and the second half indicates how long that action should be undertaken.

If you look in defines. The default value is 0. Larger values speed up the process but may get the network trapped in a local minimum. Or, even worse, the network may not converge at all. Figure 9. Recording and Transforming the Mouse Data To make a gesture, the user depresses the right mouse button and draws a pattern.

The gesture is finished when the user releases the right mouse button. A Supervised Training Approach Unfortunately, this vector can be any size at all, depending entirely on how long the user keeps the mouse button depressed. This is a problem because the number of inputs into a neural network is fixed.

We, therefore, need to find a way of reducing the number of points in the path to a fixed predetermined size. This will help the user to make more consistent gestures. As discussed earlier, the example program uses an ANN with 24 inputs representing 12 vectors.

To make 12 vectors, you need 13 points see Figure 9. The two end points of the span are then deleted. This procedure reduces the number of points by one. The process is repeated until only the required number of points remains. First sort them into order sort vecNeurons. Evolving Neural Network Topology mum.

Unfortunately, this means there is a high probability of the new individual dying out before it has time to evolve any potentially interesting behavior. This is obviously undesirable—some way has to be found of protecting the new innovation in the early days of its evolution.

This is where simulating speciation comes in handy… Speciation, as the name suggests, is the separation of a population into species. The question of what exactly is a species, is still one the biologists and other scientists are arguing over, but one of the popular definitions is: A species is a group of populations with similar characteristics that are capable of successfully interbreeding with each other to produce healthy, fertile offspring, but are reproductively isolated from other species.

In nature, a common mechanism for speciation is provided by changes in geography. Over time, these populations will diversify because of different natural selection pressures and because of different mutations within their chromosomes. On one side of the mountain, the critters may start growing thicker fur to cope with a colder climate, and on the other, they may adapt to become better at avoiding the multitude of predators that lurk there.

Eventually, the two populations will have changed so much from each other that if they ever did come into contact again, it would be impossible for them to mate successfully and have offspring. NEAT simulates speciation to provide evolutionary niches for any new topological change. This way, similar individuals only have to compete among themselves and not with the rest of the population. Therefore, they are protected somewhat from premature extinction.

A record of all the species created is kept in a class called— wait for it—CSpecies. Each epoch, every individual is tested against the first member in each species and a compatibility distance is calculated. If the compatibility distance Team LRN Evolving Neural Network Topology The CSpecies Class Once an individual has been assigned to a species, it may only mate with other members of the same species.

However, speciation alone does not protect new innovation within the population. To do that, we must somehow find a way of adjusting the fitnesses of each individual in a way that aids younger, more diverse genomes to remain active for a reasonable length of time. The technique NEAT uses to do this is called explicit fitness sharing. With NEAT, fitness scores are shared by members of the same species. NOTE In the original implementation of NEAT, the designers incorporated inter-species mating although the probability of this happening was set very low.

Although I have never observed any noticeable performance increase when using it, it may be a worthwhile exercise for you to try this out when you start fooling around with your own implementations. In addition, young species are given a fitness boost prior to the fitness sharing calculation. Likewise, old species are penalized. If a species does not show an improvement over a certain number of generations the default is 15 , then it is killed off.

The exception to this is if the species contains the best performing individual found so far, in which case the species is allowed to live. Largest first friend bool operator rhs. Epoch is part of the Cga class, which is the class that manipulates all the genomes, species, and innovations. The program then examines each species in turn and deletes all of its members apart from the best performing one. You use this individual as the genome to be tested against when the compatibility distances are calculated.

Evolving Neural Network Topology outputs. This is to prevent any dependencies on the order the training data is presented.

Training data is usually presented to a network sequentially because doing it randomly would slow down the learning considerably. For example, imagine presenting a training set consisting of a number of points lying on the circumference of a circle. If the network is not flushed, NEAT might add recurrent connections that make use of the data stored from the previous update.

This would be okay if you wanted a network that simply mapped inputs to outputs, but most often you will require the network to generalize. As before, the F key speeds up the evolution, the R key resets it, and the B key shows the best four minesweepers from the previous generation.

This time there is also an additional window created in which the phenotypes of the four best minesweepers are drawn, as shown in Figure Figure Excitory forward connections are shown in gray and inhibitory forward connections are shown in yellow. Excitory recurrent connections are shown in red and inhibitory connections are shown in blue.